Creating an Easy to Use and High Performance Parallel Platform on Multi-cores Networks

  • Viet Hai HaEmail author
  • Xuan Huyen Do
  • Van Long Tran
  • Éric Renault
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10026)


How to easily exploit the performance of network using multi-core processors nodes is the purpose of many researches including CAPE (Checkpointing Aided Parallel Execution). CAPE uses the checkpointing technique to bring the simplicity and high performance of OpenMP – a high performance and easy-to-use standard of parallel programming API on shared-memory architecture – onto distributed-memory architectures. Theoretical analysis and experimental results have proved that CAPE has ability of providing a high performance and complete compatibility with OpenMP standard. This article aims at introducing how to use multiple processes on calculating nodes to increase performance of CAPE with the initial results.


CAPE Checkpointing Aided Parallel Execution OpenMP Parallel programming Distributed computing HPC 


  1. 1.
    OpenMP API: OpenMP application programming interface 4.5 (2015)Google Scholar
  2. 2.
    Morin, C., Lottiaux, R., Vallée, G., Gallard, P., Utard, G., Badrinath, R., Rilling, L.: Kerrighed: a single system image cluster operating system for high performance computing. In: Kosch, H., Böszörményi, L., Hellwagner, H. (eds.) Euro-Par 2003. LNCS, vol. 2790, pp. 1291–1294. Springer, Heidelberg (2003). doi: 10.1007/978-3-540-45209-6_175 CrossRefGoogle Scholar
  3. 3.
    Sato, M., Harada, H., Hasegawa, A., Ishikawa, Y.: Cluster-enabled OpenMP: an OpenMP compiler for the SCASH software distributed shared memory system. Sci. Program. 9(2, 3), 123–130 (2001)Google Scholar
  4. 4.
    Basumallik, A., Eigenmann, R.: Towards automatic translation of OpenMP to MPI. In: Proceedings of 19th Annual International Conference on Supercomputing, pp. 189–198. ACM (2005)Google Scholar
  5. 5.
    Dorta, A.J., Badía, J.M., Quintana, E.S., de Sande, F.: Implementing OpenMP for clusters on top of MPI. In: Martino, B., Kranzlmüller, D., Dongarra, J. (eds.) EuroPVM/MPI 2005. LNCS, vol. 3666, pp. 148–155. Springer, Heidelberg (2005). doi: 10.1007/11557265_22 CrossRefGoogle Scholar
  6. 6.
    Huang, L., Chapman, B., Liu, Z.: Towards a more efficient implementation of OpenMP for clusters via translation to global arrays. Parallel Comput. 31(10), 1114–1139 (2005)CrossRefGoogle Scholar
  7. 7.
    Hoeflinger, J.P.: Extending OpenMP to clusters. White paper (2006)Google Scholar
  8. 8.
    Renault, É.: Distributed implementation of OpenMP based on checkpointing aided parallel execution. In: Chapman, B., Zheng, W., Gao, G.R., Sato, M., Ayguadé, E., Wang, D. (eds.) IWOMP 2007. LNCS, vol. 4935, pp. 195–206. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-69303-1_22 CrossRefGoogle Scholar
  9. 9.
    Ha, V.H., Renault, É.: Discontinuous incremental: a new approach towards extremely lightweight checkpoints. In: 2011 International Symposium on Computer Networks and Distributed Systems (CNDS), pp. 227–232, IEEE (2011)Google Scholar
  10. 10.
    Ha, V.H., Renault, E.: Improving performance of CAPE using discontinuous incremental checkpointing. In: 2011 IEEE 13th International Conference on High Performance Computing and Communications (HPCC), pp. 802–807, IEEE (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Viet Hai Ha
    • 1
    Email author
  • Xuan Huyen Do
    • 2
  • Van Long Tran
    • 3
  • Éric Renault
    • 3
  1. 1.College of EducationHue UniversityHué CityVietnam
  2. 2.College of SciencesHue UniversityHué CityVietnam
  3. 3.SAMOVA, Télécom SudParis, CNRSUniversité Paris-SaclayEvry CedexFrance

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